Error optimization using Bat and PSO algorithms for machine vision system based tool movement

Abstract

This paper presents a comparison of Bat and Particle Swarm Optimization (PSO) algorithms for optimization of lathe tool positional error in a developed machine vision system for determination of lathe tool position and verification. Bat algorithm is based on echolocation behavior of bats while PSO is inspired by social behavior of birds flocking in search for food. Both metaheuristic algorithms were tested on lathe tool movement ranging from 0.020 mm to 7 mm. The results for various lathe tool movements have demonstrated that Bat algorithm outperforms the PSO algorithm.